Artificial Intelligence in geospatial analysis: applications of self-organizing maps in the context of geographic information science

The size and dimensionality of available geospatial repositories increases every day, placing additional pressure on existing analysis tools, as they are expected to extract more knowledge from these databases. Most of these tools were created in a data poor environment and thus rarely address concerns of efficiency, dimensionality and automatic exploration. In addition, traditional statistical techniques present several assumptions that are not realistic in the geospatial data domain. An example of this is the statistical independence between observations required by most classical statistics methods, which conflicts with the well-known spatial dependence that exists in geospatial data.
Artificial intelligence and data mining methods constitute an alternative to explore and extract knowledge from geospatial data, which is less assumption dependent. In this thesis, we study the possible adaptation of existing general-purpose data mining tools to geospatial data analysis. The characteristics of geospatial datasets seems to be similar in many ways with other aspatial datasets for which several data mining tools have been used with success in the detection of patterns and relations. It seems, however that GISminded analysis and objectives require more than the results provided by these general tools and adaptations to meet the geographical information scientist‟s requirements are
needed. Thus, we propose several geospatial applications based on a well-known data mining method, the self-organizing map (SOM), and analyse the adaptations required in each application to fulfil those objectives and needs. Three main fields of GIScience are covered in this thesis: cartographic representation; spatial clustering and knowledge discovery; and location optimization.
In the cartographic representation field, we propose the use of SOM to build cartograms.
We use the standard SOM method for this purpose, although the cartogram construction requires new pre-processing and post-processing phases. We present several cartograms, such as the USA states and counties population cartograms, the Portuguese population cartogram and the world countries population cartogram.
The second field covered is spatial clustering and knowledge discovery from geospatial databases. Two SOM based methods were applied to achieve this goal. GeoSOM, which is a geospatial-aware variant of SOM, was extended and implemented in the GeoSOM Suite tool, providing a useful and efficient framework for knowledge extraction and spatial clustering tasks. Using a different approach, a hierarchical SOM is proposed to explore and cluster geospatial datasets. Tests are performed using Lisbon's Metropolitan Area 2001 census data.
Finally, concerning a location/allocation problem, a variant of SOM is proposed to manage a network of surveillance agents. This method is an online trajectory predictor, defining at each instant the path each agent should take to maximize the coverage of relevant events. The testing of this tool was performed based on an unmanned aerial vehicles network for maritime surveillance scenario, allowing the tracking of ships in a predefined region.